Final sample size includes 182 participants (Women: 115, Men: 65, Other: 2). 44 (24%) participants were of a Hispanic, Latino, or Spanish origin. The average age of the sample was 20 years (SD: 2, Range: 18 to 32).
| Freq | % | |
|---|---|---|
| American Indian/Alaskan | 2 | 1.10 |
| Asian | 14 | 7.69 |
| Black/AA | 2 | 1.10 |
| Multiracial | 9 | 4.95 |
| White | 155 | 85.16 |
| Total | 182 | 100.00 |
A 2-Event x 3-Task repeated measures ANOVA was used to verify that the tasks successfully elicited ERNs.
| Effect | DFn | DFd | F | p | p<.05 | pes | |
|---|---|---|---|---|---|---|---|
| 2 | Task | 2 | 362 | 10.38 | 4.96e-05 |
|
0.0543 |
| 3 | Event | 1 | 181 | 413.14 | 1.36e-48 |
|
0.6954 |
| 4 | Task x Event | 2 | 362 | 2.51 | 8.33e-02 | 0.0137 |
pes = partial eta-squared, and Greenhouse Geisser correction was applied for the effects involving task.
There are main effects of event and task. Error trials show larger amplitude than correct trials.
For the overall main effect of task, flanker trials showed smaller overall amplitude than either Stroop,t(181) = 2.54, p = 0.01, or go/nogo tasks,t(181) = 4.14, p < .001. There were no differences in overall amplitude between Stroop and go/nogo tasks,t(181) = 1.57, p = 0.12.
Manipulation check was successfully passed!
| Group-Level Reliability | Subject-Level Reliability | SME | |||
|---|---|---|---|---|---|
| Mean 95% CrI | Range | RMS | |||
| Flanker | ERN | Correct | .97 (.96, .98) | .95 to .998 | 0.53 |
| Error | .86 (.83, .89) | .65 to .99 | 1.33 | ||
| Difference | .79 (.73, .83) |
|
|
||
| Pe | Correct | .93 (.92, .95) | .88 to .998 | 0.5 | |
| Error | .89 (.87, .91) | .66 to .99 | 1.29 | ||
| Difference | .90 (.87, .92) |
|
|
||
| N2 | Congruent | .96 (.95, .96) | .35 to .99 | 0.77 | |
| Incongruent | .94 (.93, .96) | .46 to .99 | 0.82 | ||
| Difference | .42 (.29, .53) |
|
|
||
| N1 | Congruent | .91 (.89, .93) | .25 to .98 | 0.68 | |
| Incongruent | .88 (.86, .91) | .24 to .97 | 0.78 | ||
| Difference | .09 (.02, .21) |
|
|
||
| Stroop | ERN | Correct | .97 (.97, .98) | .94 to .998 | 0.51 |
| Error | .77 (.71, .81) | .68 to .99 | 1.61 | ||
| Difference | .75 (.69, .80) |
|
|
||
| Pe | Correct | .93 (.92, .95) | .89 to .997 | 0.49 | |
| Error | .86 (.83, .89) | .49 to .99 | 1.61 | ||
| Difference | .82 (.77, .85) |
|
|
||
| N2 | Congruent | .92 (.91, .94) | .65 to .99 | 0.84 | |
| Incongruent | .91 (.89, .93) | .53 to .99 | 0.88 | ||
| Neutral | .92 (.90, .93) | .64 to .99 | 0.86 | ||
| Difference | .27 (.12, .40) |
|
|
||
| N1 | Congruent | .84 (.80, .87) | .36 to .98 | 0.79 | |
| Incongruent | .82 (.78, .86) | .41 to .98 | 0.81 | ||
| Neutral | .81 (.77, .85) | .25 to .97 | 0.81 | ||
| Difference | .11 (.02, .24) |
|
|
||
| Go/NoGo | ERN | Correct | .97 (.96, .97) | .93 to .999 | 0.52 |
| Error | .78 (.73, .83) | .62 to .99 | 1.84 | ||
| Difference | .70 (.64, .77) |
|
|
||
| Pe | Correct | .90 (.88, .92) | .87 to .995 | 0.51 | |
| Error | .86 (.83, .89) | .75 to .996 | 1.81 | ||
| Difference | .83 (.80, .87) |
|
|
||
| N2 | Go | .96 (.95, .97) | .83 to .99 | 0.48 | |
| NoGo | .85 (.82, .88) | .50 to .98 | 2.38 | ||
| Difference | .72 (.66, .78) |
|
|
||
| N1 | Go | .91 (.89, .93) | .71 to .98 | 0.47 | |
| NoGo | .58 (.49, .67) | .26 to .94 | 1.28 | ||
| Difference | .09 (.02, .20) |
|
|
Note: difference activity for N1 and N2 during Stroop is the incongruent minus congruent activity.
| ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Flanker | ERN | .68 | |||||||||||
| Pe | .04 | .74 | |||||||||||
| N2 | .60** | .12 | .88 | ||||||||||
| N1 | .13 | -.11 | .30** | .76 | |||||||||
| Stroop | ERN | .45** | -.08 | .33** | -.10 | .52 | |||||||
| Pe | .04 | .41** | .05 | -.05 | -.05 | .65 | |||||||
| N2 | .44** | -.11 | .64** | .28** | .49** | -.11 | .80 | ||||||
| N1 | .11 | -.16* | .15* | .62** | -.02 | -.17* | .29** | .64 | |||||
| Go/NoGo | ERN | .53** | .03 | .46** | .05 | .45** | .01 | .46** | .14 | .53 | |||
| Pe | .11 | .67** | .10 | -.15* | .00 | .53** | -.11 | -.19** | .09 | .72 | |||
| N2 | .45** | .00 | .52** | .08 | .27** | .03 | .50** | .17* | .60** | .06 | .74 | ||
| N1 | .12 | -.01 | .16* | .49** | .00 | -.10 | .24** | .51** | .10 | -.09 | .32** | .40 |
| ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Flanker | ERN | (.59, .75) | |||||||||||
| Pe | (-.10, .19) | (.67, .80) | |||||||||||
| N2 | (.49, .68) | (-.03, .26) | (.84, .91) | ||||||||||
| N1 | (-.01, .27) | (-.25, .04) | (.16, .43) | (.69, .81) | |||||||||
| Stroop | ERN | (.32, .56) | (-.22, .07) | (.19, .45) | (-.24, .05) | (.40, .62) | |||||||
| Pe | (-.10, .19) | (.28, .52) | (-.10, .19) | (-.19, .10) | (-.19, .10) | (.56, .73) | |||||||
| N2 | (.31, .55) | (-.25, .04) | (.55, .72) | (.14, .41) | (.37, .59) | (-.25, .04) | (.74, .85) | ||||||
| N1 | (-.04, .25) | (-.29, -.01) | (.00, .29) | (.53, .71) | (-.16, .13) | (-.31, -.02) | (.15, .42) | (.55, .72) | |||||
| Go/NoGo | ERN | (.42, .63) | (-.12, .17) | (.34, .57) | (-.10, .19) | (.32, .55) | (-.13, .16) | (.34, .57) | (-.01, .28) | (.42, .63) | |||
| Pe | (-.04, .25) | (.58, .74) | (-.05, .24) | (-.29, -.01) | (-.14, .15) | (.41, .62) | (-.25, .04) | (-.33, -.05) | (-.06, .23) | (.64, .78) | |||
| N2 | (.33, .56) | (-.14, .15) | (.40, .62) | (-.06, .23) | (.13, .40) | (-.11, .18) | (.38, .60) | (.02, .30) | (.50, .68) | (-.09, .20) | (.67, .80) | ||
| N1 | (-.03, .26) | (-.15, .14) | (.01, .30) | (.37, .59) | (-.14, .15) | (-.24, .05) | (.10, .37) | (.39, .61) | (-.04, .24) | (-.23, .06) | (.18, .44) | (.27, .52) |
| Flanker | Stroop | Go/NoGo | |||||
|---|---|---|---|---|---|---|---|
| ERN | Pe | ERN | Pe | ERN | Pe | ||
| Flanker | ERN | (0.67, 0.89) | |||||
| Pe | (0.08, 0.60) | (0.77, 0.93) | |||||
| Stroop | ERN | (0.08, 0.60) | (-0.02, 0.54) | (0.49, 0.82) | |||
| Pe | (-0.33, 0.27) | (0.13, 0.63) | (0.10, 0.62) | (0.34, 0.75) | |||
| Go/NoGo | ERN | (0.15, 0.65) | (-0.21, 0.39) | (0.03, 0.57) | (-0.20, 0.40) | (0.37, 0.76) | |
| Pe | (-0.19, 0.41) | (0.22, 0.69) | (-0.21, 0.39) | (0.08, 0.60) | (0.34, 0.75) | (0.55, 0.85) | |
| ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Flanker | ERN | .56 | |||||||||||
| Pe | -.18* | .75 | |||||||||||
| N2 | .06 | -.06 | .28 | ||||||||||
| N1 | .06 | .01 | .38** | -.13 | |||||||||
| Stroop | ERN | .37** | -.16* | .13 | -.10 | .51 | |||||||
| Pe | -.02 | .45** | -.08 | .00 | -.15* | .62 | |||||||
| N2 | -.05 | -.05 | .00 | .10 | .00 | .04 | .12 | ||||||
| N1 | .11 | -.06 | -.02 | .05 | -.07 | .05 | .42** | -.06 | |||||
| Go/NoGo | ERN | .36** | -.20** | .05 | -.02 | .34** | -.06 | .03 | .00 | .43 | |||
| Pe | -.08 | .66** | -.09 | -.08 | -.10 | .56** | -.10 | -.02 | -.20** | .68 | |||
| N2 | .18* | -.07 | .12 | .02 | .13 | .01 | .01 | .00 | .42** | -.05 | .57 | ||
| N1 | -.15* | .10 | .07 | -.04 | .00 | -.16* | .05 | .00 | -.06 | -.06 | .22** | -.11 |
| ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Flanker | ERN | ||||||||||||
| Pe | (-.32, -.03) | ||||||||||||
| N2 | (-.08, .21) | (-.21, .08) | |||||||||||
| N1 | (-.08, .21) | (-.13, .16) | (.24, .50) | ||||||||||
| Stroop | ERN | (.24, .49) | (-.30, -.02) | (-.02, .27) | (-.24, .05) | ||||||||
| Pe | (-.17, .12) | (.32, .56) | (-.23, .06) | (-.15, .14) | (-.29, -.00) | ||||||||
| N2 | (-.19, .10) | (-.20, .09) | (-.15, .14) | (-.04, .24) | (-.15, .14) | (-.10, .19) | |||||||
| N1 | (-.04, .25) | (-.20, .09) | (-.17, .12) | (-.10, .19) | (-.21, .08) | (-.10, .19) | (.29, .53) | ||||||
| Go/NoGo | ERN | (.22, .48) | (-.34, -.06) | (-.09, .20) | (-.17, .12) | (.21, .47) | (-.20, .09) | (-.11, .18) | (-.15, .14) | ||||
| Pe | (-.23, .06) | (.57, .74) | (-.24, .05) | (-.22, .07) | (-.24, .05) | (.45, .65) | (-.24, .04) | (-.16, .13) | (-.33, -.05) | ||||
| N2 | (.03, .32) | (-.21, .08) | (-.02, .26) | (-.13, .16) | (-.01, .27) | (-.14, .15) | (-.14, .15) | (-.14, .15) | (.29, .53) | (-.19, .10) | |||
| N1 | (-.29, -.00) | (-.05, .24) | (-.08, .21) | (-.18, .11) | (-.14, .15) | (-.29, -.01) | (-.10, .19) | (-.14, .15) | (-.21, .08) | (-.20, .09) | (.07, .35) |
| Flanker | Stroop | Go/NoGo | |||||
|---|---|---|---|---|---|---|---|
| ERN | Pe | ERN | Pe | ERN | Pe | ||
| Flanker | ERN | (0.60, 0.86) | |||||
| Pe | (-0.36, 0.24) | (0.69, 0.90) | |||||
| Stroop | ERN | (0.43, 0.80) | (-0.38, 0.22) | (0.49, 0.82) | |||
| Pe | (-0.37, 0.23) | (0.04, 0.58) | (-0.13, 0.46) | (0.27, 0.72) | |||
| Go/NoGo | ERN | (0.43, 0.80) | (-0.52, 0.04) | (0.45, 0.80) | (-0.35, 0.25) | (0.42, 0.79) | |
| Pe | (-0.36, 0.24) | (0.08, 0.60) | (-0.43, 0.16) | (-0.00, 0.55) | (-0.15, 0.44) | (0.31, 0.74) | |
Here is the brms code for analyzing the data.
library(brms)
library(cmdstanr)
load("~/rrr_ern_alldata.RData")
set_cmdstan_path("~/cmdstanr_cmdstan/cmdstan-2.29.2")
save_path <- "~/rrr_ern_brms"
n_chains <- 4
n_cores <- 4
n_iter <- 2000
n_warmup <- 8000
n_seed <- 042823
n_threads <- 6
pr_ls <- c(
set_prior("normal(0,3)", class = "b"),
set_prior("lkj(2)", class = "L"),
set_prior("student_t(10, 0, 3)", class = "sd")
)
erp_onlysngtrl$site <- ifelse(erp_onlysngtrl$subjid < 2000, "usf", "byu")
brm_fit <- brm(bf(erp ~ -1 + as.factor(cell) + site + (-1 + as.factor(cell)|subjid),
sigma ~ -1 + as.factor(cell) + site + (-1 + as.factor(cell)|subjid)),
data = erp_onlysngtrl,
family = gaussian(),
prior = pr_ls,
chains = n_chains,
cores = n_cores,
iter = n_iter + n_warmup,
warmup = n_warmup,
seed = n_seed,
sample_prior = "yes",
backend = "cmdstanr",
threads = threading(n_threads),
file = file.path(save_path,"rrr_ern_mtmm_brmls"))
summary(brm_fit)
| ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Flanker | ERN | ||||||||||||
| Pe | .02 | ||||||||||||
| N2 | .65 | .12 | |||||||||||
| N1 | .13 | -.12 | .28 | ||||||||||
| Stroop | ERN | .51 | -.13 | .37 | -.11 | ||||||||
| Pe | .04 | .46 | .08 | -.02 | -.14 | ||||||||
| N2 | .42 | -.13 | .64 | .27 | .55 | -.11 | |||||||
| N1 | .09 | -.15 | .12 | .69 | -.02 | -.18 | .28 | ||||||
| Go/NoGo | ERN | .57 | .02 | .44 | .53 | .02 | .45 | .1 | |||||
| Pe | .14 | .74 | .14 | -.14 | -.03 | .57 | -.12 | -.18 | .09 | ||||
| N2 | .43 | -.02 | .51 | .06 | .24 | .04 | .49 | .13 | .67 | .05 | |||
| N1 | .1 | -.02 | .17 | .6 | .01 | -.11 | .28 | .64 | .1 | -.14 | .4 |
| ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ERN | Pe | N2 | N1 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Flanker | ERN | ||||||||||||
| Pe | (-.14, .18) | ||||||||||||
| N2 | (.54, .75) | (-.03, .26) | |||||||||||
| N1 | (-.03, .28) | (-.27, .03) | (.13, .41) | ||||||||||
| Stroop | ERN | (.35, .64) | (-.30, .05) | (.21, .51) | (-.28, .06) | ||||||||
| Pe | (-.13, .19) | (.30, .59) | (-.07, .23) | (-.17, .15) | (-.31, .04) | ||||||||
| N2 | (.28, .55) | (-.27, .03) | (.54, .73) | (.12, .41) | (.41, .67) | (-.27, .04) | |||||||
| N1 | (-.07, .25) | (-.31, .01) | (-.03, .27) | (.58, .79) | (-.20, .15) | (-.34, -.02) | (.14, .43) | ||||||
| Go/NoGo | ERN | (.42, .69) | (-.15, .19) | (.30, .57) | (-.17, .16) | (.36, .67) | (-.15, .19) | (.30, .58) | (-.08, .27) | ||||
| Pe | (-.02, .30) | (.63, .82) | (-.01, .28) | (-.30, .02) | (-.21, .14) | (.43, .69) | (-.27, .03) | (-.34, -.02) | (-.08, .25) | ||||
| N2 | (.28, .56) | (-.17, .14) | (.39, .62) | (-.10, .21) | (.07, .40) | (-.12, .20) | (.37, .61) | (-.02, .28) | (.54, .78) | (-.10, .21) | |||
| N1 | (-.10, .29) | (-.21, .17) | (-.01, .34) | (.43, .74) | (-.20, .22) | (-.30, .08) | (.10, .46) | (.48, .78) | (-.10, .30) | (-.32, .05) | (.22, .57) |
| Simple Correlation | Flanker: ERN vs. Pe | Stroop: ERN vs. Pe | Go/NoGo: ERN vs. Pe | |
|---|---|---|---|---|
| Effect | 95% Credible Interval | 95% Credible Interval | 95% Credible Interval | 95% Credible Interval |
| ERN Flanker vs. ERN Stroop | (.35, .64) | (0.26, 0.71) | (0.42, 0.86) | (0.19, 0.63) |
| ERN Flanker vs. ERN Go/NoGo | (.42, .69) | (0.34, 0.75) | (0.48, 0.92) | (0.27, 0.68) |
| ERN Stroop vs. ERN Go/NoGo | (.36, .67) | (0.28, 0.73) | (0.44, 0.88) | (0.20, 0.66) |
| Pe Flanker vs. Pe Stroop | (.30, .59) | (0.22, 0.64) | (0.36, 0.82) | (0.15, 0.58) |
| Pe Flanker vs. Pe Go/NoGo | (.63, .82) | (0.54, 0.89) | (0.68, 1.07) | (0.46, 0.84) |
| Pe Stroop vs. Pe Go/NoGo | (.43, .69) | (0.35, 0.75) | (0.49, 0.92) | (0.27, 0.69) |
| Flanker: ERN vs. Pe | (-.14, .18) | |||
| Stroop: ERN vs. Pe | (-.31, .04) | |||
| Go/NoGo: ERN vs. Pe | (-.08, .25) |
The Simple Correlation column is the correlation for the
Effect in that row. So, the first value is the correlation
between ERN flanker and ERN Stroop.
The remaining columns represent the contrast between the correlation
in the Effect column and that particular column. For
example, the (.26, .71) in the
Flanker: ERN vs. Pe column is the contrast between the
correlation for ERN Flanker vs. ERN Stroop and
Flanker: ERN vs. Pe. These are the 95% credible intervals
of the posterior distributions of the differences and whether they
exceed zero (e.g., does the difference in the posterior distributions of
two correlations exclude 0?). If they exclude 0, they are bolded.
| Effect | Simple Correlation | ERN vs. N2 | Pe vs. N2 |
|---|---|---|---|
| Flanker: ERN vs. Pe | (-.14, .18) | (-0.32, 0.02) | (0.17, 0.59) |
| Flanker: ERN vs. N2 | (.54, .75) | ||
| Flanker: Pe vs. N2 | (-.03, .26) | ||
| Stroop: ERN vs. Pe | (-.31, .04) | (-0.90, -0.46) | (-0.21, 0.16) |
| Stroop: ERN vs. N2 | (.41, .67) | ||
| Stroop: Pe vs. N2 | (-.27, .04) | ||
| Go/NoGo: ERN vs. Pe | (-.08, .25) | (-0.78, -0.38) | (-0.13, 0.21) |
| Go/NoGo: ERN vs. N2 | (.54, .78) | ||
| Go/NoGo: Pe vs. N2 | (-.10, .21) |
| Effect | ERN vs. N1 | Pe vs. N1 | N2 vs. N1 |
|---|---|---|---|
| Flanker: ERN vs. Pe | (-0.34, 0.13) | (-0.07, 0.35) | (-0.47, -0.04) |
| Flanker: ERN vs. N2 | (0.36, 0.69) | (0.59, 0.96) | (0.20, 0.55) |
| Flanker: Pe vs. N2 | (-0.23, 0.22) | (0.06, 0.43) | (-0.37, 0.06) |
| Stroop: ERN vs. Pe | (-0.38, 0.15) | (-0.20, 0.29) | (-0.65, -0.18) |
| Stroop: ERN vs. N2 | (0.38, 0.76) | (0.52, 0.93) | (0.06, 0.46) |
| Stroop: Pe vs. N2 | (-0.33, 0.16) | (-0.13, 0.27) | (-0.61, -0.17) |
| Go/NoGo: ERN vs. Pe | (-0.28, 0.26) | (-0.02, 0.48) | (-0.55, -0.06) |
| Go/NoGo: ERN vs. N2 | (0.36, 0.77) | (0.59, 1.02) | (0.06, 0.49) |
| Go/NoGo: Pe vs. N2 | (-0.31, 0.20) | (-0.02, 0.40) | (-0.59, -0.10) |
| N | Mean | SD | Min | Q1 | Median | Q3 | Max | |||
|---|---|---|---|---|---|---|---|---|---|---|
| Internalizing Raw | 182 | 15.46 | 10.57 | 1 | 7 | 13 | 21 | 49 | ||
| Externalizing Raw | 182 | 7.73 | 5.67 | 0 | 4 | 7 | 10 | 31 | ||
| Attention Problems Raw | 182 | 7.49 | 4.67 | 0 | 4 | 7 | 10 | 24 | ||
| Thought Problems Raw | 182 | 2.72 | 2.26 | 0 | 1 | 2 | 4 | 13 | ||
| Internalizing T | 182 | 54.21 | 10.88 | 32 | 46 | 54 | 61 | 82 | ||
| Externalizing T | 182 | 47.60 | 8.44 | 30 | 43 | 48 | 53 | 70 | ||
| Attention Problems T | 182 | 56.47 | 6.82 | 50 | 51 | 56 | 59 | 87 | ||
| Thought Problems T | 182 | 54.98 | 6.30 | 50 | 50 | 52 | 58 | 83 |
Here is the linear model for ERN.
| ERN | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | 3.40 | -8.69 – 15.50 | 0.581 |
| Task: Go/NoGo | -3.17 | -6.00 – -0.34 | 0.028 |
| Task: Stroop | -0.47 | -3.30 – 2.36 | 0.743 |
| Internalizing | -0.04 | -0.24 – 0.15 | 0.672 |
| Externalizing | -0.03 | -0.25 – 0.20 | 0.817 |
| Attention Problems | -0.06 | -0.13 – 0.01 | 0.104 |
| Thought Problems | 0.02 | -0.06 – 0.09 | 0.632 |
| Task: Go/NoGo x Internalizing | -0.01 | -0.07 – 0.04 | 0.588 |
| Task: Stroop x Internalizing | 0.01 | -0.04 – 0.06 | 0.658 |
| Task: Go/NoGo x Externalizing | 0.06 | -0.00 – 0.13 | 0.064 |
| Task: Stroop x Externalizing | -0.02 | -0.08 – 0.05 | 0.648 |
| Internalizing x Externalizing | 0.00 | -0.00 – 0.00 | 0.713 |
| Random Effects | |||
| σ2 | 4.98 | ||
| τ00 subjid | 4.44 | ||
| ICC | 0.47 | ||
| N subjid | 182 | ||
| Observations | 546 | ||
| Marginal R2 / Conditional R2 | 0.034 / 0.489 | ||
But please note the ANOVA for the model did not yield significant effects so we shouldn’t interpret any lower-order effects.
| Sum Sq | Mean Sq | NumDF | DenDF | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Task | 28.103 | 14.052 | 2 | 364 | 2.82091 | 0.0609 |
| Internalizing | 0.951 | 0.951 | 1 | 182 | 0.19082 | 0.6628 |
| Externalizing | 0.048 | 0.048 | 1 | 182 | 0.00964 | 0.9219 |
| Attention Problems | 13.238 | 13.238 | 1 | 182 | 2.65753 | 0.1048 |
| Thought Problems | 1.141 | 1.141 | 1 | 182 | 0.22906 | 0.6328 |
| Task x Internalizing | 4.839 | 2.419 | 2 | 364 | 0.48569 | 0.6157 |
| Task x Externalizing | 29.929 | 14.964 | 2 | 364 | 3.00416 | 0.0508 |
| Internalizing x Externalizing | 0.674 | 0.674 | 1 | 182 | 0.13531 | 0.7134 |
Here is the linear model for \(\Delta\)ERN.
| ERN_Difference | |||
|---|---|---|---|
| Predictors | Estimates | 95% CI | p |
| Intercept | -7.70 | -18.39 – 2.99 | 0.158 |
| Task: Go/NoGo | -1.94 | -4.89 – 1.01 | 0.197 |
| Task: Stroop | -0.98 | -3.93 – 1.97 | 0.515 |
| Internalizing | 0.09 | -0.08 – 0.27 | 0.306 |
| Externalizing | 0.09 | -0.11 – 0.29 | 0.368 |
| Attention Problems | -0.02 | -0.08 – 0.05 | 0.604 |
| Thought Problems | 0.01 | -0.06 – 0.07 | 0.808 |
| Task: Go/NoGo x Internalizing | -0.01 | -0.06 – 0.05 | 0.820 |
| Task: Stroop x Internalizing | 0.04 | -0.02 – 0.09 | 0.161 |
| Task: Go/NoGo x Externalizing | 0.04 | -0.03 – 0.11 | 0.279 |
| Task: Stroop x Externalizing | -0.03 | -0.10 – 0.04 | 0.346 |
| Internalizing x Externalizing | -0.00 | -0.01 – 0.00 | 0.389 |
| Random Effects | |||
| σ2 | 5.42 | ||
| τ00 subjid | 2.92 | ||
| ICC | 0.35 | ||
| N subjid | 182 | ||
| Observations | 546 | ||
| Marginal R2 / Conditional R2 | 0.025 / 0.367 | ||
Similarly, the ANOVA for the model did not yield significant effects.
| Sum Sq | Mean Sq | NumDF | DenDF | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Task | 9.047 | 4.523 | 2 | 364 | 0.8351 | 0.435 |
| Internalizing | 7.331 | 7.331 | 1 | 182 | 1.3534 | 0.246 |
| Externalizing | 4.746 | 4.746 | 1 | 182 | 0.8761 | 0.351 |
| Attention Problems | 1.457 | 1.457 | 1 | 182 | 0.2690 | 0.605 |
| Thought Problems | 0.319 | 0.319 | 1 | 182 | 0.0589 | 0.809 |
| Task x Internalizing | 16.932 | 8.466 | 2 | 364 | 1.5629 | 0.211 |
| Task x Externalizing | 22.295 | 11.148 | 2 | 364 | 2.0580 | 0.129 |
| Internalizing x Externalizing | 4.034 | 4.034 | 1 | 182 | 0.7447 | 0.389 |
In short, the symptoms did not significantly predict ERN or \(\Delta\)ERN.
Here is the script that was used for the exploratory analyses.
#which_model input defined which model to run from those described below.
which_model <- commandArgs(trailingOnly = TRUE)
which_model <- as.numeric(which_model[1])
library(brms)
library(cmdstanr)
load("~/rrr_ern_alldata.RData")
set_cmdstan_path("~/cmdstanr_cmdstan/cmdstan-2.29.2")
save_path <- "~/rrr_ern_brms"
n_chains <- 4
n_cores <- 4
n_iter <- 2000
n_warmup <- 18000
n_seed <- 042823
n_threads <- 6
pr_ls <- c(
set_prior("normal(0,3)", class = "b"),
set_prior("student_t(10, 0, 3)", class = "sd"),
set_prior("student_t(10, 0, 3)", class = "sigma")
)
erp_onlysngtrl$site <- ifelse(erp_onlysngtrl$subjid < 2000, "usf", "byu")
erp_onlysngtrl <- erp_onlysngtrl[
erp_onlysngtrl$cell %in% c("flk_err_ern", "flk_err_pe", "gng_err_ern", "gng_err_pe",
"str_err_ern", "str_err_pe", "flk_inc_n1", "flk_inc_n2",
"gng_ng_n1", "gng_ng_n2",
"str_inc_n1", "str_inc_n2"),
]
split_column <- strsplit(erp_onlysngtrl$cell, "_")
erp_onlysngtrl$task <- sapply(split_column, `[`, 1)
erp_onlysngtrl$comp <- sapply(split_column, `[`, 3)
erp_onlysngtrl$task <- as.factor(erp_onlysngtrl$task)
erp_onlysngtrl$comp <- as.factor(erp_onlysngtrl$comp)
erp_onlysngtrl$subjid <- as.factor(erp_onlysngtrl$subjid)
if (which_model == 1) {
erp_onlysngtrl <- erp_onlysngtrl[erp_onlysngtrl$comp == "ern",]
brm_fit_ern <- brm(erp ~ 1 + site +
(1|subjid) +
(1|task) +
(1|task:subjid),
data = erp_onlysngtrl,
family = gaussian(),
prior = pr_ls,
chains = n_chains,
cores = n_cores,
iter = n_iter + n_warmup,
warmup = n_warmup,
seed = n_seed,
sample_prior = "yes",
backend = "cmdstanr",
threads = threading(n_threads),
adapt_delta = .99,
max_treedepth = 15,
file = file.path(save_path,"rrr_ern_mtmm_expl_ern"))
summary(brm_fit_ern)
} else if (which_model == 2) {
erp_onlysngtrl <- erp_onlysngtrl[erp_onlysngtrl$comp == "pe",]
brm_fit_pe <- brm(erp ~ 1 + site +
(1|subjid) +
(1|task) +
(1|task:subjid),
data = erp_onlysngtrl,
family = gaussian(),
prior = pr_ls,
chains = n_chains,
cores = n_cores,
iter = n_iter + n_warmup,
warmup = n_warmup,
seed = n_seed,
sample_prior = "yes",
backend = "cmdstanr",
threads = threading(n_threads),
adapt_delta = .99,
max_treedepth = 15,
file = file.path(save_path,"rrr_ern_mtmm_expl_pe"))
summary(brm_fit_pe)
} else if (which_model == 3) {
erp_onlysngtrl <- erp_onlysngtrl[erp_onlysngtrl$comp == "n2",]
brm_fit_n2 <- brm(erp ~ 1 + site +
(1|subjid) +
(1|task) +
(1|task:subjid),
data = erp_onlysngtrl,
family = gaussian(),
prior = pr_ls,
chains = n_chains,
cores = n_cores,
iter = n_iter + n_warmup,
warmup = n_warmup,
seed = n_seed,
sample_prior = "yes",
backend = "cmdstanr",
threads = threading(n_threads),
adapt_delta = .99,
max_treedepth = 15,
file = file.path(save_path,"rrr_ern_mtmm_expl_n2"))
summary(brm_fit_n2)
} else if (which_model == 4) {
erp_onlysngtrl <- erp_onlysngtrl[erp_onlysngtrl$comp == "n1",]
brm_fit_n1 <- brm(erp ~ 1 + site +
(1|subjid) +
(1|task) +
(1|task:subjid),
data = erp_onlysngtrl,
family = gaussian(),
prior = pr_ls,
chains = n_chains,
cores = n_cores,
iter = n_iter + n_warmup,
warmup = n_warmup,
seed = n_seed,
sample_prior = "yes",
backend = "cmdstanr",
threads = threading(n_threads),
adapt_delta = .99,
max_treedepth = 15,
file = file.path(save_path,"rrr_ern_mtmm_expl_n1"))
summary(brm_fit_n1)
}
| Group | ICC |
|---|---|
| subjid | 0.0531850 |
| task | 0.0152521 |
| task:subjid | 0.0337091 |
| Group | ICC |
|---|---|
| subjid | 0.0847282 |
| task | 0.0589856 |
| task:subjid | 0.0473605 |
| Group | ICC |
|---|---|
| subjid | 0.0605298 |
| task | 0.0246928 |
| task:subjid | 0.0353104 |
| Group | ICC |
|---|---|
| subjid | 0.0293998 |
| task | 0.0049109 |
| task:subjid | 0.0121961 |
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] performance_0.10.2 papeR_1.0-5 xtable_1.8-4
## [4] car_3.1-1 carData_3.0-5 patchwork_1.1.2
## [7] sjlabelled_1.2.0 sjmisc_2.8.9 sjPlot_2.8.12
## [10] lmerTest_3.1-3 lme4_1.1-32 Matrix_1.5-3
## [13] ez_4.4-0 brms_2.19.0 Rcpp_1.0.10
## [16] lubridate_1.9.1 forcats_0.5.2 stringr_1.5.0
## [19] dplyr_1.1.1 purrr_1.0.1 readr_2.1.3
## [22] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
## [25] tidyverse_1.3.2.9000 here_1.0.1
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.3 tidyselect_1.2.0 htmlwidgets_1.6.2
## [4] grid_4.1.2 munsell_0.5.0 effectsize_0.8.2
## [7] codetools_0.2-18 DT_0.27 miniUI_0.1.1.1
## [10] withr_2.5.0 Brobdingnag_1.2-9 colorspace_2.1-0
## [13] highr_0.10 knitr_1.42 rstudioapi_0.14
## [16] stats4_4.1.2 bayesplot_1.10.0 emmeans_1.8.5
## [19] rstan_2.21.8 farver_2.1.1 datawizard_0.7.0
## [22] bridgesampling_1.1-2 rprojroot_2.0.3 coda_0.19-4
## [25] vctrs_0.6.2 generics_0.1.3 TH.data_1.1-1
## [28] xfun_0.37 timechange_0.2.0 R6_2.5.1
## [31] markdown_1.5 gamm4_0.2-6 projpred_2.3.0
## [34] cachem_1.0.7 promises_1.2.0.1 scales_1.2.1
## [37] multcomp_1.4-23 nnet_7.3-18 gtable_0.3.3
## [40] processx_3.8.1 sandwich_3.0-2 rlang_1.1.1
## [43] systemfonts_1.0.4 splines_4.1.2 broom_1.0.4
## [46] rapportools_1.1 checkmate_2.2.0 inline_0.3.19
## [49] yaml_2.3.7 reshape2_1.4.4 abind_1.4-5
## [52] modelr_0.1.10 threejs_0.3.3 crosstalk_1.2.0
## [55] backports_1.4.1 httpuv_1.6.9 Hmisc_5.0-1
## [58] tensorA_0.36.2 tools_4.1.2 tcltk_4.1.2
## [61] ellipsis_0.3.2 kableExtra_1.3.4 multilevel_2.7
## [64] jquerylib_0.1.4 posterior_1.4.1 plyr_1.8.8
## [67] base64enc_0.1-3 ps_1.7.5 prettyunits_1.1.1
## [70] rpart_4.1.19 summarytools_1.0.1 zoo_1.8-11
## [73] cluster_2.1.4 magrittr_2.0.3 data.table_1.14.8
## [76] magick_2.7.4 gmodels_2.18.1.1 colourpicker_1.2.0
## [79] mvtnorm_1.1-3 matrixStats_0.63.0 hms_1.1.2
## [82] shinyjs_2.1.0 mime_0.12 evaluate_0.20
## [85] shinystan_2.6.0 sjstats_0.18.2 gridExtra_2.3
## [88] ggeffects_1.2.0 rstantools_2.3.0 compiler_4.1.2
## [91] psychometric_2.3 psychReport_3.0.2 crayon_1.5.2
## [94] minqa_1.2.5 StanHeaders_2.21.0-7 htmltools_0.5.5
## [97] mgcv_1.8-41 later_1.3.0 tzdb_0.3.0
## [100] Formula_1.2-5 RcppParallel_5.1.7 DBI_1.1.3
## [103] MASS_7.3-58.2 boot_1.3-28.1 cli_3.6.1
## [106] pryr_0.1.6 gdata_2.18.0.1 parallel_4.1.2
## [109] insight_0.19.1 igraph_1.4.1 pkgconfig_2.0.3
## [112] numDeriv_2016.8-1.1 foreign_0.8-84 xml2_1.3.3
## [115] dygraphs_1.1.1.6 svglite_2.1.1 bslib_0.4.2
## [118] webshot_0.5.4 estimability_1.4.1 rvest_1.0.3
## [121] distributional_0.3.2 callr_3.7.3 digest_0.6.31
## [124] parameters_0.20.1 rmarkdown_2.20 htmlTable_2.4.1
## [127] shiny_1.7.4 gtools_3.9.4 nloptr_2.0.3
## [130] lifecycle_1.0.3 nlme_3.1-161 jsonlite_1.8.4
## [133] cmdstanr_0.5.3 viridisLite_0.4.2 fansi_1.0.4
## [136] pillar_1.9.0 lattice_0.20-45 loo_2.5.1
## [139] fastmap_1.1.1 httr_1.4.4 pkgbuild_1.4.0
## [142] survival_3.5-0 glue_1.6.2 xts_0.13.0
## [145] bayestestR_0.13.0 shinythemes_1.2.0 pander_0.6.5
## [148] stringi_1.7.12 sass_0.4.5